Search Results for author: Zhe Li

Found 74 papers, 26 papers with code

Low-Resource Text Classification via Cross-lingual Language Model Fine-tuning

no code implementations CCL 2020 Xiuhong Li, Zhe Li, Jiabao Sheng, Wushour Slamu

There are major challenges of low-resource agglutinative text classification the lack of labeled data in a target domain and morphologic diversity of derivations in language structures.

Language Modelling Morphological Analysis +2

RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction

1 code implementation ACL 2022 Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou

However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.

A Graph Reconstruction by Dynamic Signal Coefficient for Fault Classification

no code implementations30 May 2023 Wenbin He, Jianxu Mao, Yaonan Wang, Zhe Li, Qiu Fang, Haotian Wu

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model.

feature selection Graph Reconstruction

Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

1 code implementation18 May 2023 Zhe Li, shiyi qi, Yiduo Li, Zenglin Xu

In this paper, we thoroughly investigate the intrinsic effectiveness of recent approaches and make three key observations: 1) linear mapping is critical to prior long-term time series forecasting efforts; 2) RevIN (reversible normalization) and CI (Channel Independent) play a vital role in improving overall forecasting performance; and 3) linear mapping can effectively capture periodic features in time series and has robustness for different periods across channels when increasing the input horizon.

Time Series Time Series Forecasting

Caption Anything: Interactive Image Description with Diverse Multimodal Controls

1 code implementation4 May 2023 Teng Wang, Jinrui Zhang, Junjie Fei, Hao Zheng, Yunlong Tang, Zhe Li, Mingqi Gao, Shanshan Zhao

Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, $\textit{e. g.}$, looking at the specified regions or telling in a particular text style.

controllable image captioning Instruction Following

PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling

1 code implementation25 Apr 2023 Zhe Li, Zerong Zheng, Yuxiao Liu, Boyao Zhou, Yebin Liu

To this end, we present PoseVocab, a novel pose encoding method that encourages the network to discover the optimal pose embeddings for learning the dynamic human appearance.

Track Anything: Segment Anything Meets Videos

1 code implementation24 Apr 2023 Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao, Fangjing Wang, Feng Zheng

Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos.

Image Segmentation Semantic Segmentation +1

From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels

1 code implementation ICCV 2023 Zhendong Yang, Ailing Zeng, Zhe Li, Tianke Zhang, Chun Yuan, Yu Li

We decompose the KD loss and find the non-target loss from it forces the student's non-target logits to match the teacher's, but the sum of the two non-target logits is different, preventing them from being identical.

Self-Knowledge Distillation

Balancing Privacy Protection and Interpretability in Federated Learning

no code implementations16 Feb 2023 Zhe Li, Honglong Chen, Zhichen Ni, Huajie Shao

Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information.

Federated Learning

MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing

1 code implementation9 Feb 2023 Zhe Li, Zhongwen Rao, Lujia Pan, Zenglin Xu

Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence.

Multivariate Time Series Forecasting Time Series

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

no code implementations21 Jan 2023 Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.

Contrastive Learning Multivariate Time Series Forecasting +2

Resource-Efficient RGBD Aerial Tracking

1 code implementation CVPR 2023 Jinyu Yang, Shang Gao, Zhe Li, Feng Zheng, Aleš Leonardis

However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view.

Object Tracking

Learning Dual-Fused Modality-Aware Representations for RGBD Tracking

no code implementations6 Nov 2022 Shang Gao, Jinyu Yang, Zhe Li, Feng Zheng, Aleš Leonardis, Jingkuan Song

However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored.

Object Tracking

Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space

no code implementations29 Oct 2022 Zhe Li, Man-Wai Mak, Helen Mei-Ling Meng

The challenges in applying contrastive learning to speaker verification (SV) are that the softmax-based contrastive loss lacks discriminative power and that the hard negative pairs can easily influence learning.

Contrastive Learning Speaker Verification

Speaker Representation Learning via Contrastive Loss with Maximal Speaker Separability

1 code implementation29 Oct 2022 Zhe Li, Man-Wai Mak

A great challenge in speaker representation learning using deep models is to design learning objectives that can enhance the discrimination of unseen speakers under unseen domains.

Contrastive Learning Data Augmentation +1

Changer: Feature Interaction is What You Need for Change Detection

1 code implementation17 Sep 2022 Sheng Fang, Kaiyu Li, Zhe Li

To verify the effectiveness of MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple interaction strategies: Aggregation-Distribution (AD) and "exchange".

Building change detection for remote sensing images Change Detection

Rethinking Knowledge Distillation via Cross-Entropy

1 code implementation22 Aug 2022 Zhendong Yang, Zhe Li, Yuan Gong, Tianke Zhang, Shanshan Lao, Chun Yuan, Yu Li

Furthermore, we smooth students' target output to treat it as the soft target for training without teachers and propose a teacher-free new KD loss (tf-NKD).

Knowledge Distillation

Prompting for Multi-Modal Tracking

no code implementations29 Jul 2022 Jinyu Yang, Zhe Li, Feng Zheng, Aleš Leonardis, Jingkuan Song

Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking.

AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture

1 code implementation5 Jul 2022 Zhe Li, Zerong Zheng, Hongwen Zhang, Chaonan Ji, Yebin Liu

Then given a monocular RGB video of this subject, our method integrates information from both the image observation and the avatar prior, and accordingly recon-structs high-fidelity 3D textured models with dynamic details regardless of the visibility.

Masked Generative Distillation

3 code implementations3 May 2022 Zhendong Yang, Zhe Li, Mingqi Shao, Dachuan Shi, Zehuan Yuan, Chun Yuan

The current distillation algorithm usually improves students' performance by imitating the output of the teacher.

Image Classification Instance Segmentation +4

RGBD Object Tracking: An In-depth Review

1 code implementation26 Mar 2022 Jinyu Yang, Zhe Li, Song Yan, Feng Zheng, Aleš Leonardis, Joni-Kristian Kämäräinen, Ling Shao

Particularly, we are the first to provide depth quality evaluation and analysis of tracking results in depth-friendly scenarios in RGBD tracking.

Object Tracking

SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text

no code implementations23 Feb 2022 Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Zhe Li, Dezhi Peng

Specifically, we propose a style bank to parameterize the specific handwriting styles as latent vectors, which are input to a generator as style priors to achieve the corresponding handwritten styles.

Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks

no code implementations22 Feb 2022 Zhe Li, Andreas S. Tolias, Xaq Pitkow

In this work we trained graph neural networks to fit time series from an example nonlinear dynamical system, the belief propagation algorithm.

Inductive Bias Time Series +1

Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation

no code implementations16 Feb 2022 Honglong Chen, Zhe Li, Zhu Wang, Zhichen Ni, Junjian Li, Ge Xu, Abdul Aziz, Feng Xia

As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.

Movie Recommendation Recommendation Systems

VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation

no code implementations16 Feb 2022 Zhu Wang, Honglong Chen, Zhe Li, Kai Lin, Nan Jiang, Feng Xia

Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.

Descriptive Movie Recommendation +1

Focal and Global Knowledge Distillation for Detectors

1 code implementation CVPR 2022 Zhendong Yang, Zhe Li, Xiaohu Jiang, Yuan Gong, Zehuan Yuan, Danpei Zhao, Chun Yuan

Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation.

Image Classification Knowledge Distillation +2

The emergence of cooperation from shared goals in the Systemic Sustainability Game of common pool resources

no code implementations1 Oct 2021 Chengyi Tu, Paolo DOdorico, Zhe Li, Samir Suweis

The sustainable use of common-pool resources (CPRs) is a major environmental governance challenge because of their possible over-exploitation.

Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras

no code implementations ICCV 2021 Yuxiang Zhang, Zhe Li, Liang An, Mengcheng Li, Tao Yu, Yebin Liu

Overall, we propose the first light-weight total capture system and achieves fast, robust and accurate multi-person total motion capture performance.

3D Multi-Person Pose Estimation

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter

1 code implementation CVPR 2021 Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Dezhi Peng, Zhe Li, Mengchao He, Yongpan Wang, Canjie Luo

Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC).

Optical Character Recognition Optical Character Recognition (OCR) +1

Salient Positions based Attention Network for Image Classification

1 code implementation9 Jun 2021 Sheng Fang, Kaiyu Li, Zhe Li

Aimed at both questions this paper proposes the salient positions-based attention scheme SPANet, which is inspired by some interesting observations on the attention maps and affinity matrices generated in self-attention scheme.

Classification Image Classification

CARLS: Cross-platform Asynchronous Representation Learning System

1 code implementation26 May 2021 Chun-Ta Lu, Yun Zeng, Da-Cheng Juan, Yicheng Fan, Zhe Li, Jan Dlabal, Yi-Ting Chen, Arjun Gopalan, Allan Heydon, Chun-Sung Ferng, Reah Miyara, Ariel Fuxman, Futang Peng, Zhen Li, Tom Duerig, Andrew Tomkins

In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.

Representation Learning

ConTNet: Why not use convolution and transformer at the same time?

2 code implementations27 Apr 2021 Haotian Yan, Zhe Li, Weijian Li, Changhu Wang, Ming Wu, Chuang Zhang

It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.

Image Classification object-detection +1

Class-Incremental Learning with Generative Classifiers

1 code implementation20 Apr 2021 Gido M. van de Ven, Zhe Li, Andreas S. Tolias

As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y).

class-incremental learning Class Incremental Learning +1

POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

no code implementations CVPR 2021 Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu

By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions.

3D Reconstruction

Temporal Action Segmentation from Timestamp Supervision

1 code implementation CVPR 2021 Zhe Li, Yazan Abu Farha, Juergen Gall

To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations.

Action Segmentation Weakly Supervised Action Localization

Siamese NestedUNet Networks for Change Detection of High Resolution Satellite Image

1 code implementation27 Oct 2020 Kaiyu Li, Zhe Li, Sheng Fang

In this paper, we improve the semantic segmentation network UNet++ and propose a fully convolutional siamese network (Siam-NestedUNet) for change detection.

Change Detection Change detection for remote sensing images +2

Joint Multi-Dimension Pruning via Numerical Gradient Update

no code implementations18 May 2020 Zechun Liu, Xiangyu Zhang, Zhiqiang Shen, Zhe Li, Yichen Wei, Kwang-Ting Cheng, Jian Sun

To tackle these three naturally different dimensions, we proposed a general framework by defining pruning as seeking the best pruning vector (i. e., the numerical value of layer-wise channel number, spacial size, depth) and construct a unique mapping from the pruning vector to the pruned network structures.

Robust 3D Self-portraits in Seconds

no code implementations CVPR 2020 Zhe Li, Tao Yu, Chuanyu Pan, Zerong Zheng, Yebin Liu

In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera.

RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies

no code implementations7 Mar 2020 Zhe Li, Chunhua Sun, Chunli Liu, Xiayu Chen, Meng Wang, Yezheng Liu

To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy.

Outlier Detection

Long Short-Term Sample Distillation

no code implementations2 Mar 2020 Liang Jiang, Zujie Wen, Zhongping Liang, Yafang Wang, Gerard de Melo, Zhe Li, Liangzhuang Ma, Jiaxing Zhang, Xiaolong Li, Yuan Qi

The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.

CircConv: A Structured Convolution with Low Complexity

no code implementations28 Feb 2019 Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, Bo Yuan

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications.

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

no code implementations12 Dec 2018 Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, Yanzhi Wang

It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Adaptive Negative Curvature Descent with Applications in Non-convex Optimization

no code implementations NeurIPS 2018 Mingrui Liu, Zhe Li, Xiaoyu Wang, Jin-Feng Yi, Tianbao Yang

Negative curvature descent (NCD) method has been utilized to design deterministic or stochastic algorithms for non-convex optimization aiming at finding second-order stationary points or local minima.

Generative Adversarial Active Learning for Unsupervised Outlier Detection

2 code implementations28 Sep 2018 Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, Xiangnan He

In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.

Active Learning Binary Classification +1

A Unified Analysis of Stochastic Momentum Methods for Deep Learning

no code implementations30 Aug 2018 Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang

However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored.

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch

no code implementations CVPR 2019 Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran

In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain.

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints

no code implementations3 Jun 2018 Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang

In this paper, we study how to design a genetic programming approach for optimizing the structure of a CNN for a given task under limited computational resources yet without imposing strong restrictions on the search space.

Evolutionary Algorithms

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

no code implementations10 May 2018 Zhe Li, Ji Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Qinru Qiu, Bo Yuan, Yanzhi Wang

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications.

Learning Topics using Semantic Locality

no code implementations11 Apr 2018 Ziyi Zhao, Krittaphat Pugdeethosapol, Sheng Lin, Zhe Li, Caiwen Ding, Yanzhi Wang, Qinru Qiu

The topic modeling discovers the latent topic probability of the given text documents.

Topic Models

C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

no code implementations20 Mar 2018 Zhe Li, Xiaolong Ma, Hongjia Li, Qiyuan An, Aditya Singh Rathore, Qinru Qiu, Wenyao Xu, Yanzhi Wang

It is of vital importance to enable 3D printers to identify the objects to be printed, so that the manufacturing procedure of an illegal weapon can be terminated at the early stage.

Action Detection Activity Detection +1

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

no code implementations20 Mar 2018 Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.

Model Compression Time Series +1

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

no code implementations14 Mar 2018 Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang

The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.

A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

no code implementations25 Feb 2018 Kun Hu, Zhe Li, Ying Liu, Luyin Cheng, Qi Yang, Yan Li

In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn.

Causal Inference Management +1

Image Dataset for Visual Objects Classification in 3D Printing

no code implementations15 Feb 2018 Hongjia Li, Xiaolong Ma, Aditya Singh Rathore, Zhe Li, Qiyuan An, Chen Song, Wenyao Xu, Yanzhi Wang

The rapid development in additive manufacturing (AM), also known as 3D printing, has brought about potential risk and security issues along with significant benefits.

Classification General Classification

An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing

no code implementations3 Feb 2018 Xiaolong Ma, Yi-Peng Zhang, Geng Yuan, Ao Ren, Zhe Li, Jie Han, Jingtong Hu, Yanzhi Wang

However, in these works, the memory design optimization is neglected for weight storage, which will inevitably result in large hardware cost.

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer

no code implementations9 Sep 2017 Tianbao Yang, Zhe Li, Lijun Zhang

In this paper, we present a simple analysis of {\bf fast rates} with {\it high probability} of {\bf empirical minimization} for {\it stochastic composite optimization} over a finite-dimensional bounded convex set with exponential concave loss functions and an arbitrary convex regularization.

SEP-Nets: Small and Effective Pattern Networks

no code implementations13 Jun 2017 Zhe Li, Xiaoyu Wang, Xutao Lv, Tianbao Yang

By doing this, we show that previous deep CNNs such as GoogLeNet and Inception-type Nets can be compressed dramatically with marginal drop in performance.

Binarization Quantization

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

no code implementations13 Mar 2017 Ning Liu, Zhe Li, Zhiyuan Xu, Jielong Xu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system.

Cloud Computing Decision Making +3

Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

no code implementations12 Mar 2017 Ji Li, Zihao Yuan, Zhe Li, Caiwen Ding, Ao Ren, Qinru Qiu, Jeffrey Draper, Yanzhi Wang

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks.

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

no code implementations ICML 2017 Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Victor Pan, Bo Yuan

Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks.

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

no code implementations18 Nov 2016 Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

Stochastic Computing (SC), which uses bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has a high potential for implementing DCNNs with high scalability and ultra-low hardware footprint.

Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

no code implementations12 Apr 2016 Tianbao Yang, Qihang Lin, Zhe Li

This paper fills the gap between practice and theory by developing a basic convergence analysis of two stochastic momentum methods, namely stochastic heavy-ball method and the stochastic variant of Nesterov's accelerated gradient method.

Improved Dropout for Shallow and Deep Learning

no code implementations NeurIPS 2016 Zhe Li, Boqing Gong, Tianbao Yang

To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization.

Stochastic Optimization

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